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Found 20 papers, 8 papers with code
Comprehension Based Question Answering using Bloom’s Taxonomy
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge.
BloomVQA: Assessing Hierarchical Multi-modal Comprehension
We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks.
A Video is Worth 10,000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval
We use synthetic captions from this pipeline to perform a benchmark of a representative set of video language models using long video datasets, and show that the models struggle on shorter captions.
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences.
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
Based on this pipeline and the existing coarse-grained annotated dataset, we build the CURE benchmark to measure both the zero-shot reasoning performance and consistency of VLMs.
Probing Conceptual Understanding of Large Visual-Language Models
In this work we focus on conceptual understanding of these large V+L models.
Unpacking Large Language Models with Conceptual Consistency
The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query.
Trigger Hunting with a Topological Prior for Trojan Detection
Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.
Improving Users' Mental Model with Attention-directed Counterfactual Edits
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs.
Comprehension Based Question Answering using Bloom's Taxonomy
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge.
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Can we develop visually grounded dialog agents that can efficiently adapt to new tasks without forgetting how to talk to people?
Emergence of Compositional Language with Deep Generational Transmission
In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language.
Grad-CAM: Why did you say that?
We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
For captioning and VQA, we show that even non-attention based models can localize inputs.
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on CUB-200-2011
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions.
Reducing Overfitting in Deep Networks by Decorrelating Representations
One major challenge in training Deep Neural Networks is preventing overfitting.
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks.
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation
We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models.
